SkillClaw
Frameworks & Eval · tested 2026-06-16 · re-test due 2026-09-16 · by the Hlido desk, not the vendor
In short: Research-grade collective skill evolution for AI agents — 1,900 stars and an arXiv paper make this more than a weekend project, but it's a research tool, not a production one.
5 PASS · 0 FAIL of 5 public-surface claims
Quick answer
SkillClaw scores 70/100 (STEADY) on Hlido’s independent, hands-on test (reviewed 2026-06-16). STEADY (70) because the arXiv publication gives it more credibility than typical OSS projects, 1,901 stars signal real ML community interest, and the collective learning across agents/devices is a genuinely differentiate Pricing: Open source (free entry point documented).
SkillClaw addresses a real problem that most agent frameworks ignore: agents learn nothing from their interactions. Every session starts fresh, making the same mistakes and rediscovering the same solutions. SkillClaw's approach — collective skill evolution where agent skills improve from every interaction and share that learning across agents, sessions, and devices — is genuinely novel and backed by a published arXiv paper (2604.08377). The 1,901 GitHub stars for a research-lab project suggest the ML community takes the approach seriously. Compatibility with Hermes, OpenClaw, QwenPaw, IronClaw, PicoClaw, and ZeroClaw shows ecosystem investment beyond a single-paper demo. What's cautious here: the gap between research results (which are often measured on controlled benchmarks) and production reliability (which depends on real user interactions, edge cases, and adversarial inputs) is large. SkillClaw's value proposition is specifically that skills evolve from 'real interactions' — which means early users are essentially contributing training signal, with all the quality variance that implies. For teams that want continual skill improvement and are willing to operate in a research-grade framework, SkillClaw is the most thoughtful solution in this space.
Why STEADY
STEADY (70) because the arXiv publication gives it more credibility than typical OSS projects, 1,901 stars signal real ML community interest, and the collective learning across agents/devices is a genuinely differentiated capability. Not VITAL because it's a research tool with production reliability questions, and the skill evolution effectiveness in uncontrolled environments is unverified from the public surface.
Public-surface checklist
- PASS Homepage loads (required)
- PASS Primary value prop (required) — 'Let Skills Evolve Collectively with Agentic Evolver'
- PASS Cta present (required) — Quick install command in README
- PASS Pricing or access — Free and open source (MIT)
- PASS Evidence or demo — arXiv paper + Hugging Face paper page
What we saw
1 screenshot captured by the Hlido engine during the reviewed run (run-3ffa6d56e0067a6e-github-com). Our own captures — not vendor marketing material.
What it does well
- arXiv paper (2604.08377) gives academic credibility and a verifiable methodology description
- Collective skill sharing — one agent's learning improves all agents in the ecosystem
- Quick install: `npx skills add SkillClaw -y -g` — zero friction for supported platforms
- Multi-platform compatibility: Hermes, OpenClaw, QwenPaw, IronClaw, PicoClaw, ZeroClaw
- Open source (MIT) with real CI — not just a paper demo
What it fails at
- Production reliability of continually-evolving skills in uncontrolled environments unverified
- Skill quality depends on contributing users — early ecosystem = noisy training signal
- GitHub login wall prevented T2 testing of the actual skill execution
- Adversarial skill injection (what happens when bad actors contribute garbage interactions) not addressed in public docs
- Dependency on other agents in the ecosystem for collective value — isolated use is weaker
Best for
- ML researchers building on agent skill learning foundations
- Developers already using Hermes or OpenClaw agents who want continual improvement
- Projects where the same task types recur at scale and improvement from iteration is valuable
- Research teams that want a published-method foundation rather than proprietary black-box learning
Not recommended for
- Production systems where skill quality variance is unacceptable
- One-off or low-volume agent tasks (collective learning needs volume to show value)
- Teams wanting a standalone agent framework — SkillClaw is a plugin layer, not a full framework
- Security-sensitive deployments without vetted skill provenance controls
Pricing & access
- ModelOpen source
- Free entry pointYes — a free tier or open-source edition is documented
- Pricing findable on the public surfacePASS Free and open source (MIT) (tested 2026-06-16)
Derived from Hlido-held evidence only (engine checklist + editorial text); quotes are verbatim from the scorecard; not vendor-supplied; re-derived daily. Verify current prices on the vendor's pricing page. Last verified 2026-06-16.
Compared to
-
YantrikDB Hermes Plugin
collective-skill-evolution
Both integrate with Hermes-family agents for persistent memory/skill functions. YantrikDB focuses on explicit memory management for individual agents. SkillClaw focuses on collective, implicit skill learning across the ecosystem. YantrikDB is more predictable; SkillClaw's collective value is higher at scale.
Agent relevance
CLI SDK
Agentic-Commerce Readiness 49/100 · SURFACE-ONLY
Independent readiness for agent delegation & transaction. How it’s scored · check live
Skill plugin for Hermes/OpenClaw and compatible agents. Install via npx skills add. Agents call SkillClaw's skill-store endpoints to retrieve learned skills. Collective evolution happens server-side. No standalone API for external agent consumption.
Agent-friendly score: 6/10
Score over time
The longitudinal record — every point is the score as published on that date. Raw series.
Evidence
scorecard.json · transparency passport · registry · methodology
Embed this trust badge
Markdown
[](https://hlido.eu/check/?agent=amap-ml-skillclaw)HTML
<a href="https://hlido.eu/check/?agent=amap-ml-skillclaw"><img src="https://hlido.eu/badge/amap-ml-skillclaw.svg" alt="Hlido trust score"></a>